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Registro Completo |
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Biblioteca(s): |
Biblioteca Rui Tendinha. |
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Data corrente: |
16/02/2016 |
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Data da última atualização: |
31/03/2016 |
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Autoria: |
VARGAS, A. A. T.; SANTOS, A. F. dos.; PACOVA, B. E. V.; SILVEIRA, J. S. M. |
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Afiliação: |
Alvaro Augusto Teixeira Vargas, EMCAPA; Alvaro Figueredo dos Santos, EMCAPA; Braz Eduardo Vieira Pacova, EMCAPA; José Sebastião Machado Silveira, EMCAPA. |
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Título: |
Fixação simbiótica do nitrogênio no feijoeiro : seleção de cultivares para alta eficiência na fixação do N² e resistência à antracnose no Espírito Santo. |
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Ano de publicação: |
1983 |
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Fonte/Imprenta: |
COMUNICADO TÉCNICO, n. 17, p. 1-6, maio. 1983. |
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Série: |
(EMCAPA. Comunicado Técnico, 17). |
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ISSN: |
0101-7683 |
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Idioma: |
Português |
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Conteúdo: |
O trabalho foi realizado com o objetivo de selecionar cultivares de feijão adaptadas às condições do estado do Espírito Santo, que tenham eficiência na fixação simbiótica do nitrogênio atmosférico, bem como resistência à antracnose (Colletotrichum lindemuthianum), principal doença de caráter econômico nesta cultura no estado. |
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Palavras-Chave: |
Comunicado Técnico; Emcapa; Estado do Espírito Santo; Fixação simbiótica; Nitrogênio no feijoeiro; Resistência à Antracnose; Seleção de cultivares para alta eficiência. |
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Categoria do assunto: |
-- |
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URL: |
http://biblioteca.incaper.es.gov.br/digital/bitstream/item/1235/1/BRT-comunicadotecnico-n17-Emcapa.pdf
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Marc: |
LEADER 01270naa a2200265 a 4500
001 1009665
005 2016-03-31
008 1983 bl uuuu u00u1 u #d
022 $a0101-7683
100 1 $aVARGAS, A. A. T.
245 $aFixação simbiótica do nitrogênio no feijoeiro$bseleção de cultivares para alta eficiência na fixação do N² e resistência à antracnose no Espírito Santo.$h[electronic resource]
260 $c1983
490 $a(EMCAPA. Comunicado Técnico, 17).
520 $aO trabalho foi realizado com o objetivo de selecionar cultivares de feijão adaptadas às condições do estado do Espírito Santo, que tenham eficiência na fixação simbiótica do nitrogênio atmosférico, bem como resistência à antracnose (Colletotrichum lindemuthianum), principal doença de caráter econômico nesta cultura no estado.
653 $aComunicado Técnico
653 $aEmcapa
653 $aEstado do Espírito Santo
653 $aFixação simbiótica
653 $aNitrogênio no feijoeiro
653 $aResistência à Antracnose
653 $aSeleção de cultivares para alta eficiência
700 1 $aSANTOS, A. F. dos.
700 1 $aPACOVA, B. E. V.
700 1 $aSILVEIRA, J. S. M.
773 $tCOMUNICADO TÉCNICO$gn. 17, p. 1-6, maio. 1983.
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Registro original: |
Biblioteca Rui Tendinha (BRT) |
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Biblioteca |
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Registro |
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Fechar
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Registro Completo |
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Biblioteca(s): |
Biblioteca Rui Tendinha. |
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Data corrente: |
18/02/2020 |
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Data da última atualização: |
18/02/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
ESGARIO, J. G. M.; KROHLING, R. A.; VENTURA, J. A. |
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Afiliação: |
José G. M. Esgario; Renato A. Krohling; Jose Aires Ventura, Incaper. |
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Título: |
Deep learning for classification and severity estimation of coffee leaf biotic stress. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Computers and Electronics in Agriculture, v. 169, fev. 2020. |
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DOI: |
https://doi.org/10.1016/j.compag.2019.105162 |
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Idioma: |
Inglês |
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Conteúdo: |
Biotic stress consists of damage to plants through other living organisms. The efficient control of biotic agents such as pests and pathogens (viruses, fungi, bacteria, etc.) is closely related to the concept of agricultural sustainability. Agricultural sustainability promotes the development of new technologies that allow the reduction of environmental impacts, greater accessibility to farmers and, consequently, increased productivity. The use of computer vision with deep learning methods allows the early and correct identification of the stress-causing agent. So, corrective measures can be applied as soon as possible to mitigate the problem. The objective of this work is to design an effective and practical system capable of identifying and estimating the stress severity caused by biotic agents on coffee leaves. The proposed approach consists of a multi-task system based on convolutional neural networks. In addition, we have explored the use of data augmentation techniques to make the system more robust and accurate. Computational experiments performed with the proposed system using the ResNet50 architecture obtained an accuracy of for the biotic stress classification and for severity estimation. Moreover, it was found that by classifying only the symptoms, the results were greater than . The experimental results indicate that the proposed system might be a suitable tool to assist both experts and farmers in the identification and quantification of biotic stresses in coffee plantations. MenosBiotic stress consists of damage to plants through other living organisms. The efficient control of biotic agents such as pests and pathogens (viruses, fungi, bacteria, etc.) is closely related to the concept of agricultural sustainability. Agricultural sustainability promotes the development of new technologies that allow the reduction of environmental impacts, greater accessibility to farmers and, consequently, increased productivity. The use of computer vision with deep learning methods allows the early and correct identification of the stress-causing agent. So, corrective measures can be applied as soon as possible to mitigate the problem. The objective of this work is to design an effective and practical system capable of identifying and estimating the stress severity caused by biotic agents on coffee leaves. The proposed approach consists of a multi-task system based on convolutional neural networks. In addition, we have explored the use of data augmentation techniques to make the system more robust and accurate. Computational experiments performed with the proposed system using the ResNet50 architecture obtained an accuracy of for the biotic stress classification and for severity estimation. Moreover, it was found that by classifying only the symptoms, the results were greater than . The experimental results indicate that the proposed system might be a suitable tool to assist both experts and farmers in the identification and quantification of biotic stresses in cof... Mostrar Tudo |
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Palavras-Chave: |
Biotic stress; Control of biotic; Convolutional neural networks. |
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Categoria do assunto: |
-- |
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URL: |
https://biblioteca.incaper.es.gov.br/digital/bitstream/123456789/3972/1/Coffee-leaves-stress-ventura.pdf
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Marc: |
LEADER 02151naa a2200193 a 4500
001 1022121
005 2020-02-18
008 2020 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.compag.2019.105162$2DOI
100 1 $aESGARIO, J. G. M.
245 $aDeep learning for classification and severity estimation of coffee leaf biotic stress.$h[electronic resource]
260 $c2020
520 $aBiotic stress consists of damage to plants through other living organisms. The efficient control of biotic agents such as pests and pathogens (viruses, fungi, bacteria, etc.) is closely related to the concept of agricultural sustainability. Agricultural sustainability promotes the development of new technologies that allow the reduction of environmental impacts, greater accessibility to farmers and, consequently, increased productivity. The use of computer vision with deep learning methods allows the early and correct identification of the stress-causing agent. So, corrective measures can be applied as soon as possible to mitigate the problem. The objective of this work is to design an effective and practical system capable of identifying and estimating the stress severity caused by biotic agents on coffee leaves. The proposed approach consists of a multi-task system based on convolutional neural networks. In addition, we have explored the use of data augmentation techniques to make the system more robust and accurate. Computational experiments performed with the proposed system using the ResNet50 architecture obtained an accuracy of for the biotic stress classification and for severity estimation. Moreover, it was found that by classifying only the symptoms, the results were greater than . The experimental results indicate that the proposed system might be a suitable tool to assist both experts and farmers in the identification and quantification of biotic stresses in coffee plantations.
653 $aBiotic stress
653 $aControl of biotic
653 $aConvolutional neural networks
700 1 $aKROHLING, R. A.
700 1 $aVENTURA, J. A.
773 $tComputers and Electronics in Agriculture$gv. 169, fev. 2020.
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